Clinical Characteristics of Adipsic Diabetes Insipidus.

OBJECTIVE: Adipsic diabetes insipidus (ADI) is a life-threatening disease. It is characterized by arginine vasopressin deficiency and thirst absence. Data about clinical characteristics of ADI were scarce. This study investigated the clinical features of hospitalized ADI patients. METHODS: A retrospective study was conducted of hospitalized ADI patients admitted to the Endocrinology Department of Huashan Hospital between January 2014 and December 2021, and compared with central diabetes insipidus (CDI) patients with normal thirst. RESULTS: During the study period, there were a total of 507 hospitalized CDI patients, among which 50 cases were ADI, accounting for 9.9%. Forty percent of ADI patients were admitted due to hypernatremia, but there were no admissions due to hypernatremia in the control group. The lesions of ADI patients were more likely to be located in the suprasellar area (100% vs 66%, P < .05). Higher prevalence of hypothalamic dysfunction (76% vs 8%, P < .001), central hypothyroidism (100% vs 90%, P = .031), hyperglycemia (66% vs 32%, P < .001), dyslipidemia (92% vs 71%, P = .006), and hyperuricemia (64% vs 37%, P = .003) was found in the ADI group than in the control group. The proportions of hypernatremia were higher in the ADI group both at admission and at discharge (90% vs 8%, 68% vs 8%, respectively, both with P < .001), contributing to higher prevalence of complications, such as renal insufficiency, venous thrombosis, and infection. CONCLUSION: ADI patients were found with higher prevalence of hypernatremia, hypopituitarism, hypothalamic dysfunction, metabolic disorders, and complications, posing a great challenge for comprehensive management.

Multi-Step Continuous-Flow Organic Synthesis: Opportunities and Challenges.

Continuous-flow multi-step synthesis takes the advantages of microchannel flow chemistry and may transform the conventional multi-step organic synthesis by using integrated synthetic systems. To realize the goal, however, innovative chemical methods and techniques are urgently required to meet the significant remaining challenges. In the past few years, by using green reactions, telescoped chemical design, and/or novel in-line separation techniques, major and rapid advancement has been made in this direction. This minireview summarizes the most recent reports (2017-2020) on continuous-flow synthesis of functional molecules. Notably, several complex active pharmaceutical ingredients (APIs) have been prepared by the continuous-flow approach. Key technologies to the successes and remaining challenges are discussed. These results exemplified the feasibility of using modern continuous-flow chemistry for complex synthetic targets, and bode well for the future development of integrated, automated artificial synthetic systems.

Value of carotid corrected flow time or changes value of FTc could be more useful in predicting fluid responsiveness in patients undergoing robot-assisted gynecologic surgery: a prospective observational study.

Background: The aim of this study was to evaluate the ability of point-of-care Doppler ultrasound measurements of carotid corrected flow time and its changes induced by volume expansion to predict fluid responsiveness in patients undergoing robot-assisted gynecological surgery. Methods: In this prospective study, carotid corrected flow time was measured using Doppler images of the common carotid artery before and after volume expansion. The stroke volume index at each time point was recorded using noninvasive cardiac output monitoring with MostCare. Of the 52 patients enrolled, 26 responded. Results: The areas under the receiver operating characteristic curves of the carotid corrected flow time and changes in carotid corrected flow time induced by volume expansion were 0.82 and 0.67, respectively. Their optimal cut-off values were 357 and 19.5 ms, respectively. Conclusion: Carotid corrected flow time was superior to changes in carotid corrected flow time induced by volume expansion for predicting fluid responsiveness in this population.

Nomogram for predicting the risk of postoperative delirium in elderly patients undergoing orthopedic surgery.

OBJECTIVE: To retrospectively analyze the risk factors for postoperative delirium (POD) after orthopedic surgery in elderly patients and establish an individualized nomogram to predict the risk of POD. METHODS: The data of 1011 patients who underwent orthopedic surgery from January 2019 to January 2022 were retrospectively analyzed. Univariate and multivariate logistic analyses were used to screen for independent risk factors. Stepwise regression was conducted to screen risk factors to construct a nomogram to predict the risk of POD after orthopedic surgery in elderly individuals, and nomogram validation analyses were performed. RESULTS: The logistic regression results showed that age (≥ 75 years old vs. < 75 years old; odds ratio (OR) = 2.889; 95% confidence interval (CI), 1.149, 7.264), sex (male vs. female, OR = 2.368; 95% CI, 1.066, 5.261), and preoperative cognitive impairment (yes vs. no, OR = 13.587; 95% CI, 4.360, 42.338) were independent risk factors for POD in elderly patients who underwent orthopedic surgery (P < 0.05). A nomogram was constructed using 7 risk factors, i.e., age, American Society of Anesthesiologists (ASA) classification, sex, preoperative hemoglobin (Hb), preoperative pulmonary disease, cognitive impairment, and intraoperative infusion volume. The area under the curve (AUC) showed good discrimination (0.867), the slope of the calibration curve was 1.0, and the optimal net benefit of the nomogram from the decision curve analysis (DCA) was 0.01-0.58. CONCLUSION: This study used 7 risk factors to construct a nomogram to predict the risk of POD after major orthopedic surgery in elderly individuals, and the nomogram had good discrimination ability, accuracy, and clinical practicability.

Recent Advances of Chiral Isolated and Small Organic Molecules: Structure and Properties for Circularly Polarized Luminescence.

This paper explores recent advancements in the field of circularly polarized luminescence (CPL) exhibited by small and isolated organic molecules. The development and application of small CPL molecule are systematically reviewed through eight different chiral skeleton sections. Investigating the intricate interplay between molecular structure and CPL properties, the paper aims at providing and enlighting novel strategies for CPL-based applications.

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.

We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.