Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists. Lung cancer screening has now been widely applied because of its effectiveness and ease of performance. More than 10 million chest CT scans were performed in the United States alone in 2012, highlighting the potential for this clinical scenario 1. Radiologists who evaluate a large number of low-dose CT (LDCT) screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. 2-5. Artificial intelligence (AI) is showing rapid advantages and exciting achievements in the fields of imaging diagnosis and/or evaluation 6-12. AI detection of lung nodules has long been expected to be an effective assistant in daily clinical practice, especially for LDCT lung nodule screening. Thus far, many novel deep neural networkbased systems have demonstrated the potential for use in the proposed technique for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness 13-18. However, the majority of the proposed systems were trained on CT scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC-IDRI), the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database 19-24. The effectiveness in clinical practice has not been fully recognized or proven. From the previous study, the prevalence of pulmonary nodules (PNs) varies greatly in different populations, ranging from 13 to 58% 25-32. Different demographic features, selection criteria of participants, and the referral pattern of the study centre may explain such differences 27. In China, the incidence of non-c...

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ObjectiveThis study evaluated the characteristics of new users of sodium glucose co-transporter 2 inhibitors (SGLT2i) in clinical practice to assess the applicability of the findings from clinical trials (Empagliflozin, Cardiovascular Outcomes and Mortality in Type 2 Diabetes (EMPA-REG OUTCOME) trial, Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58 trial, Canagliflozin Cardiovascular Assessment Study (CANVAS) program and the Evaluation of Ertugliflozin Efficacy and Safety Cardiovascular Outcomes (VERTIS-CV) trial) and multinational observational studies (CVD-REAL Nordic study and CVD-REAL 2 study).Research design and methodsWe conducted a retrospective cohort study using the largest electronic medical records database from seven hospitals in Taiwan. We included adult patients with type 2 diabetes initiating canagliflozin, dapagliflozin and empagliflozin between 1 January 2018 and 31 August 2019. We compared the patient characteristics with SGLT2i to examine the data representativeness of clinical trials and to evaluate channeling uses between canagliflozin, dapagliflozin and empagliflozin.ResultsWe identified a cohort of 11 650 patients newly initiating SGLT2i, 49.9% of whom received empagliflozin. However, only 18.7%, 19.2%, 50.4% and 57.3% of real-world SGLT2i new users were included in the EMPA-REG OUTCOME trial, VERTIS-CV trial, DECLARE-TIMI 58 trial and CANVAS program, respectively. Reasons for exclusion were largely reduced cardiovascular disease risks in real-world patients, namely 72.8%, 73.6% and 34.2% for EMPA-REG OUTCOME trial, VERTIS-CV trial and DECLARE-TIMI 58 trial and CANVAS program, respectively. However, hemoglobin A1c out of range accounted for the most frequent reason (25.0%) for exclusion of real-world patients from the CANVAS program. We found channeling uses in different SGLT2i, for example, more patients receiving empagliflozin (15.3%) and canagliflozin (19.6%) had poorer renal functions (eg, estimated glomerular filtration rate <60 mL/min/1.73 m2), compared with dapagliflozin (9.3%).ConclusionsThe findings provide clear evidence that results from current studies may be less applicable to real-world patients. Further studies are required to support the concept of real-world cardiovascular event protection through SGLT2i.

Rationale: Pituitary stalk interruption syndrome (PSIS) is a congenital pituitary anatomical defect. It is characterized by the triad of thin or interrupted pituitary stalk, absent or ectopic posterior lobe, and hypoplastic or aplastic anterior lobe. Moreover, this condition is considered rare. Patient concerns: A 23-year-old male patient presented with a history of short stature and hypogonadism. Laboratory assessment revealed low thyroxine, cortisol, and adrenocorticotropic hormone levels, which are consistent with adrenal insufficiency without hypoglycemia. The insulin-induced hypoglycemia tolerance test finding indicated growth hormone (GH) deficiency. Moreover, magnetic resonance imaging revealed an interrupted pituitary stalk, ectopic posterior pituitary, and hypoplastic anterior pituitary. This triad of symptoms was indicative of PSIS. Diagnosis: PSIS; hypopituitarism: secondary hypothyroidism, secondary adrenocortical dysfunction, hypogonadotropic hypogonadism, and GH deficiency; sphenoid sinus cyst; osteoporosis; hyperinsulinism; and dyslipidemia. Interventions: The patient was deficient in adrenaline, thyroxine, gonadal steroid, and GH. Thus, glucocorticoid replacement therapy was initiated, followed by euthyrox, androgen, and human chorionic gonadotropin treatment. Calcium tablets, calcitriol, and alendronate sodium were used for the management of osteoporosis. The patient was 164 cm tall, and his bone age was approximately 15 years old. However, owing to a poor economic condition, the family did not proceed with GH therapy. Outcomes: The patient did not present with adrenal or hypothyroidism crisis after receiving poly-hormonal replacement therapy. His secondary sexual characteristics began to develop. However, owing to a short treatment window period, the patient could not receive the required treatment. Hence, whether the patient would have a normal fertility function needs to be confirmed. Lessons: PSIS is a rare disease with various clinical characteristics. During the neonatal period and infancy, the signs and symptoms of PSIS are often not evident. Therefore, diagnosis is delayed. The early detection of hormone deficiency and treatment initiation can affect both the quality of life and the prognosis of patients with PSIS. Thus, the diagnosis and treatment of this disease must be improved to help patients achieve a better quality of life and to prevent reproductive health problems.