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Obesity is a risk factor for the development of gallstones. Rapid weight loss may be an even stronger risk factor. We retrospectively assessed the prevalence and risk factors of gallstone formation after adjustable gastric banding (AGB) in a Dutch population. All patients who underwent AGB between Jan 1992 and Dec 2000 for morbid obesity were invited to take part in this study. Transabdominal ultrasonography of the gallbladder was performed in those patients without a prior history of cholecystectomy (Group A). Additionally, 45 morbidly obese patients underwent ultrasonography of the gallbladder before weight reduction surgery (Group B). 120 patients were enrolled in the study (Group A). Prior history of cholecystectomy was present in 21 patients: 16 before and 5 after AGB. Ultrasonography was performed in 98 patients: gallstones were present in 26 (26.5%). On multivariate analysis, neither preoperative weight, nor maximum weight loss, nor the interval between operation and the postoperative ultrasonography were determinants of the risk for developing gallstone disease. Prevalence of gallstones was significantly lower in the morbidly obese patients who had not yet undergone weight reduction surgery (Group B). Rapid weight loss induced by AGB, is an important risk factor for the development of gallstones. No additional determinants were found. Every morbidly obese patient undergoing bariatric surgery must be considered at risk for developing gallstone disease.

The prognosis for primary CNS lymphoma has improved with the use of high-dose methotrexate-based chemotherapy, but patient outcomes remain poor. Rituximab, a chimeric monoclonal antibody that targets the CD20 cell surface protein, has substantial activity in systemic CD20-positive diffuse large B-cell lymphoma, but its efficacy in primary CNS lymphoma is unknown and low penetration of the large rituximab molecule through the blood–brain barrier could limit its effect. We aimed to investigate the addition of rituximab to a high-dose methotrexate-based chemotherapy regimen in patients with newly diagnosed primary CNS lymphoma. This intergroup, multicentre, open-label, randomised phase 3 study was done at 23 hospitals in the Netherlands, Australia, and New Zealand. Non-immunocompromised patients aged 18–70 years with newly diagnosed primary CNS lymphoma were randomly assigned (1:1) to receive methotrexate-based chemotherapy with or without intravenous rituximab. We used a web-based randomisation system with stratification by centre, age, and Eastern Cooperative Oncology Group–WHO performance status, and a minimisation procedure. All group assignment was open label and neither investigators nor patients were masked to allocation. All patients were treated with two 28-day cycles of induction chemotherapy, consisting of intravenous methotrexate 3 g per m2 on days 1 and 15, intravenous carmustine 100 mg per m2 on day 4, intravenous teniposide 100 mg per m2 on days 2 and 3, and oral prednisone 60 mg per m2 on days 1–5, with (R-MBVP) or without (MBVP) intravenous rituximab 375 mg per m2 on days 0, 7, 14, and 21 in cycle one and days 0 and 14 in cycle two. Patients with response at the end of induction subsequently received high-dose cytarabine and, in patients aged 60 years or younger, low-dose whole-brain radiotherapy. The primary endpoint was event-free survival, with events defined as not reaching complete response or complete response unconfirmed at the end of treatment, or progression or death after response; analysis was adjusted for age and performance score. Patients were analysed on a modified intention-to-treat basis. This trial is registered with the Nederlands Trial Register, number NTR2427, and the Australian New Zealand Clinical Trials Registry, number ACTRN12610000908033. The trial was closed on May 27, 2016, after achieving complete accrual, and follow-up is ongoing. Between Aug 3, 2010, and May 27, 2016, we recruited 200 patients (109 men and 91 women; median age was 61 years [IQR 55–67]). We randomly assigned 100 patients to MBVP and 99 patients to R-MBVP. One patient was randomly assigned to the R-MBVP group but found to be ineligible because of an incorrect diagnosis and was excluded from all analyses. After a median follow-up of 32·9 months (IQR 23·9–51·5), 98 patients had had an event (51 in the MBVP group and 47 in the R-MBVP group), of whom 79 had died (41 in the MBVP group and 38 in the R-MBVP group). Event-free survival at 1 year was 49% (95% CI 39–58) in the MBVP group (no rituximab) and 52% (42–61) in the R-MBVP group (with rituximab; hazard ratio 1·00, 95% CI 0·70–1·43, p=0·99). Grade 3 or 4 adverse events occurred in 58 (58%) patients in the MBVP group and 63 (64%) patients in the R-MBVP group, with infections (24 [24%] patients receiving MBVP vs 21 [21%] patients receiving R-MBVP), haematological toxicity (15 [15%] vs 12 [12%]), and nervous system disorders (ten [10%] vs 15 [15%]) being the most common. Life-threatening or fatal serious adverse events occurred in 12 (12%) patients in the MBVP group and ten (10%) patients in the R-MBVP group, and five (5%) patients in the MBVP group and three (3%) in the R-MBVP group died from treatment-related causes. We found no clear benefit of addition of rituximab to methotrexate, carmustine, teniposide, and prednisone chemotherapy in primary CNS lymphoma. Therefore, the results of this study do not support the use of rituximab as a component of standard treatment in primary CNS lymphoma. Roche, the Dutch Cancer Society, and Stichting STOPhersentumoren.

Background Venous thromboembolism is a potentially fatal complication of hospitalisation, affecting approximately 3% of non-surgical patients. Administration of low molecular weight heparins to the appropriate patients adequately decreases venous thromboembolism incidence, but guideline adherence is notoriously low. Objective To determine the effect of a multifaceted intervention on thromboprophylaxis guideline adherence. The secondary objective was to study the effect on guideline adherence specifically in patients with a high venous thromboembolism risk. As an exploratory objective, we determined how many venous thromboembolisms may be prevented. Setting A Dutch general teaching hospital. Method A prospective study with a pre- and post-intervention measurement was conducted. A multifaceted intervention, consisting of Clinical Decision Support software, a mobile phone application, monitoring of duplicate anticoagulants and training, was implemented. Guideline adherence was assessed by calculating the Padua prediction and Improve bleeding score for each patient. The number of preventable venous thromboembolisms was calculated using the incidences of venous thromboembolism in patients with and without adequate thromboprophylaxis and extrapolated to the annual number of admitted patients. Main outcome measure Adherence to thromboprophylaxis guidelines in pre- and post-intervention measurements. Results 170 patients were included: 85 in both control and intervention group. The intervention significantly increased guideline adherence from 49.4 to 82.4% (OR 4.78; 95%CI 2.37–9.63). Guideline adherence in the patient group with a high venous thromboembolism risk also increased significantly from 54.5 to 84.3% (OR 2.46; 95%CI 1.31–4.62), resulting in the potential prevention of ± 261 venous thromboembolisms per year. Conclusions Our multifaceted intervention significantly increased thromboprophylaxis guideline adherence.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here we introduce a nonlinear learning metamaterial -- an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR and nonlinear regression, without a computer. We find our nonlinear learning metamaterial reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Physical networks, such as biological neural networks, can learn desired functions without a central processor, using local learning rules in space and time to learn in a fully distributed manner. Learning approaches such as equilibrium propagation, directed aging, and coupled learning similarly exploit local rules to accomplish learning in physical networks such as mechanical, flow, or electrical networks. In contrast to certain natural neural networks, however, such approaches have so far been restricted to the quasistatic limit, where they learn on time scales slow compared to their physical relaxation. This quasistatic constraint slows down learning, limiting the use of these methods as machine learning algorithms, and potentially restricting physical networks that could be used as learning platforms. Here we explore learning in an electrical resistor network that implements coupled learning, both in the lab and on the computer, at rates that range from slow to far above the quasistatic limit. We find that up to a critical threshold in the ratio of the learning rate to the physical rate of relaxation, learning speeds up without much change of behavior or error. Beyond the critical threshold, the error exhibits oscillatory dynamics but the networks still learn successfully.