Lentis Mplus LS-313 MF20 IOL.Purpose of review The purpose of this review is to discuss the current evidence for acupuncture in migraine and to provide insight into which patients may benefit most from acupuncture. Recent findings Multiple studies support acupuncture as a validated modality for the prevention of episodic migraine. While small studies on acupuncture for the prevention of chronic migraine are encouraging, evidence for its benefit is limited. Likewise, the existing studies on acupuncture for acute migraine treatment are insufficient. Acupuncture is commonly used to treat migraine and has gained popularity as a validated non-pharmaceutical option. Although the specific mechanisms of acupuncture are not clearly understood, there is evidence for its effectiveness in treating migraine. Results of acupuncture studies are quite variable and depend on multiple factors, including frequency, duration, and consistency of treatments, as well as acupoint selection. A better understanding of the flaws inherent in acupuncture research will help guide the development of future studies.The objective of the present study was to perform a quantitative analysis of cancer stem cell (CSC) marker expression in ovarian carcinoma effusions. The clinical role of SSEA1 in metastatic high-grade serous carcinoma (HGSC) was additionally analyzed. CD133, Nanog, SOX2, Oct3/4, SSEA1, and SSEA4 protein expressions were quantitatively analyzed using flow cytometry (FCM) in 24 effusions. SSEA1 expression by immunohistochemistry was analyzed in 384 HGSC effusions. Highly variable expression of CSC markers by FCM was observed, ranging from 0 to 78% of Ber-EP4-positive cells in the case of CD133, with the largest number of negative specimens seen for SSEA4. SSEA1 expression by immunohistochemistry was found in HGSC cells in 336/384 (89%) effusions, most commonly focally ( less then 5% of cells). SSEA1 was overexpressed in post-chemotherapy disease recurrence specimens compared with chemo-naïve HGSC effusions tapped at diagnosis (p = 0.029). In univariate survival analysis, higher SSEA1 expression was significantly associated with poor overall survival (p = 0.047) and progression-free survival (p = 0.018), though it failed to retain its prognostic role in Cox multivariate survival analysis in which it was analyzed with clinical parameters (p = 0.059 and p = 0.111 for overall and progression-free survival, respectively). In conclusion, CSC markers are variably expressed in ovarian carcinoma effusions. SSEA1 expression is associated with disease progression and poor survival in metastatic HGSC. https://www.selleckchem.com/products/PP242.html Silencing this molecule may have therapeutic relevance in this cancer.The original version of this article, unfortunately, contained errors.Aims/hypothesis During pregnancy, maternal metabolic disease and hormonal imbalance may alter fetal beta cell development and/or proliferation, thus leading to an increased risk for developing type 2 diabetes in adulthood. Although thyroid hormones play an important role in fetal endocrine pancreas development, the impact of maternal hypothyroidism on glucose homeostasis in adult offspring remains poorly understood. Methods We investigated this using a mouse model of hypothyroidism, induced by administration of an iodine-deficient diet supplemented with propylthiouracil during gestation. Results Here, we show that, when fed normal chow, adult mice born to hypothyroid mothers were more glucose-tolerant due to beta cell hyperproliferation (two- to threefold increase in Ki67-positive beta cells) and increased insulin sensitivity. However, following 8 weeks of high-fat feeding, these offspring gained 20% more body weight, became profoundly hyperinsulinaemic (with a 50% increase in fasting insulin concentration), insulin-resistant and glucose-intolerant compared with controls from euthyroid mothers. Furthermore, altered glucose metabolism was maintained in a second generation of animals. Conclusions/interpretation Therefore, gestational hypothyroidism induces long-term alterations in endocrine pancreas function, which may have implications for type 2 diabetes prevention in affected individuals.Aims/hypothesis Coronavirus disease-2019 (COVID-19) is a life-threatening infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Diabetes has rapidly emerged as a major comorbidity for COVID-19 severity. However, the phenotypic characteristics of diabetes in COVID-19 patients are unknown. Methods We conducted a nationwide multicentre observational study in people with diabetes hospitalised for COVID-19 in 53 French centres in the period 10-31 March 2020. The primary outcome combined tracheal intubation for mechanical ventilation and/or death within 7 days of admission. Age- and sex-adjusted multivariable logistic regressions were performed to assess the prognostic value of clinical and biological features with the endpoint. ORs are reported for a 1 SD increase after standardisation. Results The current analysis focused on 1317 participants 64.9% men, mean age 69.8 ± 13.0 years, median BMI 28.4 (25th-75th percentile 25.0-32.7) kg/m2; with a predominance of type 2 diabetes (a (OR 2.80 [1.46, 5.38]), and microvascular (OR 2.14 [1.16, 3.94]) and macrovascular complications (OR 2.54 [1.44, 4.50]) were independently associated with the risk of death on day 7. Conclusions/interpretations In people with diabetes hospitalised for COVID-19, BMI, but not long-term glucose control, was positively and independently associated with tracheal intubation and/or death within 7 days. Trial registration clinicaltrials.gov NCT04324736.Purpose of review This paper investigates present uses and future potential of artificial intelligence (AI) applied to intracoronary imaging technologies. Recent findings Advances in data analytics and digitized medical imaging have enabled clinical application of AI to improve patient outcomes and reduce costs through better diagnosis and enhanced workflow. Applications of AI to IVUS and IVOCT have produced improvements in image segmentation, plaque analysis, and stent evaluation. Machine learning algorithms are able to predict future coronary events through the use of imaging results, clinical evaluations, laboratory tests, and demographics. The application of AI to intracoronary imaging holds significant promise for improved understanding and treatment of coronary heart disease. Even in these early stages, AI has demonstrated the ability to improve the prediction of cardiac events. Large curated data sets and databases are needed to speed the development of AI and enable testing and comparison among algorithms.